Current Applications of Machine Learning in Additive Manufacturing: A Review on Challenges and Future Trends

Abstract

The article provides a detailed review of the utilisation of machine learning (ML) in various domains of additive manufacturing (AM) and highlights its potential to address key challenges in the industry. The article acknowledges the hurdles to widespread adoption of AM, including barriers in design for AM (DfAM), limited materials selection, processing defects, and inconsistent product quality. ML is increasingly being integrated into AM workflows, offering significant potential for classification, regression, and clustering to address the AM challenges. It can be used to generate new high-performance metamaterials and optimize topological designs, improving the efficacy and usefulness of the design process. It also optimizes process parameters, monitors powder spreading, and detects in-process defects, enhancing the overall quality and reliability of the manufacturing process. ML aids in streamlining the production processes and ensuring consistent product quality. There's recognition of the importance of data security in AM, with ML techniques potentially posing risks of data breaches if not properly managed. Therefore, a synergistic approach where ML assists in identifying critical conditions and human operators take action is likely the most effective way to ensure both efficiency and accuracy in AM processes. The paper summarises the key results from the literature and discusses some significant applications of machine learning in AM. It emphasizes the potential of ML to drive innovation and address critical challenges in the AM industry. Overall, the article underscores the significance of ML in advancing AM technology and its potential to overcome existing barriers to adoption, making way for broader implementation of AM in various industries.